This is by far the first paper considering joint
optimization of link scheduling, routing and replication for
disruption-tolerant networks (DTNs). The optimization problems
for resource allocation in DTNs are typically solved using
dynamic programming which requires knowledge of future
events such as meeting schedules and durations. This paper
defines a new notion of optimality for DTNs, called snapshot
optimality where nodes are not clairvoyant, i.e., cannot look
ahead into future events, and thus decisions are made using only
contemporarily available knowledge. Unfortunately, the optimal
solution for snapshot optimality still requires solving an NPhard problem of maximum weight independent set and a global
knowledge of who currently owns a copy and what their delivery
probabilities are. This paper presents a new efficient approximation algorithm, called Distributed Max-Contribution (DMC) that
performs greedy scheduling, routing and replication based only
on locally and contemporarily available information. Through a
simulation study based on real GPS traces tracking over 4000
taxies for about 30 days in a large city, DMC outperforms existing
heuristically engineered resource allocation algorithms for DTNs.